Research Paper Advance Articles
Identification of the m6A/m5C/m1A methylation modification genes in Alzheimer’s disease based on bioinformatic analysis
- 1 Ganzhou City Key Laboratory of Mental Health, The Third People’s Hospital of Ganzhou City, Ganzhou 341000, Jiangxi, China
- 2 Guangzhou Medical University, Guangzhou 510182, Guangdong, China
- 3 Department of Medical Genetics, Ganzhou Maternal and Child Health Hospital, Ganzhou 341000, China
Received: April 22, 2024 Accepted: October 1, 2024 Published: October 31, 2024
https://doi.org/10.18632/aging.206146How to Cite
Copyright: © 2024 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: As a progressive neurodegenerative disease, the comprehensive understanding of the pathogenesis of Alzheimer’s disease (AD) is yet to be clarified. Modifications in RNA, including m6A/m5C/m1A, affect the onset and progression of many diseases. Consequently, this study focuses on the role of methylation modification in the pathogenesis of AD.
Materials and methods: Three AD-related datasets, namely GSE33000, GSE122063, and GSE44770, were acquired from GEO. Differential analysis of m6A/m5C/m1A regulator genes was conducted. Applying a consensus clustering approach, distinct subtypes within AD were identified as per the expression patterns of relevant differentially expressed genes. Machine learning models were constructed to identify five significant genes from the best model. The analysis of hub gene-based drug regulatory networks and ceRNA regulatory networks was conducted by Cytoscape.
Results: In comparison to non-AD patients, 24 genes were identified as dysregulated in AD patients, and these genes were associated with various immunological characteristics. Two distinct clusters were successfully identified through consensus clustering, with cluster 2 demonstrating higher immune characteristics compared to cluster 1. The performance of four machine learning models was determined by conducting a receiver operating characteristic (ROC) analysis. The analysis revealed that the SVM model achieved the highest AUC value of 0.947. Five genes (YTHDF1, METTL3, DNMT1, DNMT3A, ALKBH1) were selected as the predicted genes. Finally, a hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully developed.
Conclusions: The findings offered fresh perspectives on the molecular patterns and immune mechanisms underlying AD, contributing valuable insights into our understanding of this complex neurodegenerative disorder.
Abbreviations
AD: Alzheimer’s disease; ROC: receiver operating characteristic; m1A: N1-methyladenosine; m6A: N6-methyladenosine; m5C: 5-methylcytosine; MMG: methylation-modified gene; DEGs: differentially expressed genes; ssGSEA: single sample gene set enrichment analysis; CM: consensus matrix; CDF: cumulative distribution function; PCA: Principal Component Analysis; GSVA: gene set variation analysis; MSigDB: Molecular Signatures Database; GSEA: gene set enrichment analysis; RF: random forest model; SVM: support vector machine model; XGB: eXtreme Gradient Boosting; GLM: generalized linear model; ceRNA: competitive endogenous RNA.